Deploying Mobility-On-Demand for All by Optimizing Paratransit Services

August 2024·
Sophie Pavia
,
David Rogers
Amutheezan Sivagnanam
Amutheezan Sivagnanam
,
Michael Wilbur
,
Danushka Edirimanna
,
Youngseo Kim
,
Philip Pugliese
,
Samitha Samaranayake
Aron Laszka
Aron Laszka
,
Ayan Mukhopadhyay
,
Abhishek Dubey
Abstract
While on-demand ride-sharing services have become popular in recent years, traditional on-demand transit services cannot be used by everyone, e.g., wheelchair-bound people. Paratransit services, operated by public transit agencies, are a critical infrastructure that offers door-to-door transportation assistance for individuals who face challenges in using standard transit routes. However, with declining ridership and mounting financial pressure, public transit agencies in the USA struggle to operate existing services. We collaborate with a public transit agency from the southern USA, highlight the specific nuances of paratransit optimization, and present a vehicle routing problem formulation for optimizing paratransit. We validate our approach using real-world data from the transit agency, present results from an actual pilot deployment of the proposed approach in the city, and show how the proposed approach comprehensively outperforms existing approaches used by the transit agency. To the best of our knowledge, this work presents one of the first examples of using open-source algorithmic approaches for paratransit optimization.
Type
Venue
33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), 7430–7437
publications

Acceptance Rate: 20%
Note: Multi-Year Track on AI and Social Good

Authors

Amutheezan Sivagnanam
Ph.D. 2025
Amutheezan joined the Applied Artificial Intelligence Lab as a Ph.D. student in fall 2019. His research interest is in Machine Learning and Data Science. Previously, he graduated with a Bachelors’s Degree in Computer Science and Engineering from the University of Moratuwa, Sri Lanka in 2017. He worked as an intern at WSO2 Lanka (PVT) Ltd., as part of the Data Analytics Team in 2016. He also worked as a software engineer in MillenniumIT Software PVT Ltd. from 2018 to 2019. He has previously worked on a machine learning project on semi-supervised based sentiment analysis of microblogging platforms and on a data analytics project on monitoring flows of HL7 messages. Currently, his research work focuses on optimizing bus-route scheduling and minimizing energy costs.
Aron Laszka
Associate Professor

Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.

His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.

His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.